Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

# path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"
# 
# df <- path %>% 
#   paste0("latest.json") %>% 
#   readr::read_lines() %>% 
#   paste0(path, .) %>% 
#   jsonlite::fromJSON()

df <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/latest.json" %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

# path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"
# 
# df_s <- path %>% 
#   paste0("latest.json") %>% 
#   readr::read_lines() %>% 
#   paste0(path, .) %>% 
#   jsonlite::fromJSON()

df_s <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/latest.json" %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 101886

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 111804

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-12-25T18:14:23+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

　

対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

新型コロナ関連ニュース

新型コロナ関連のニュース

news <- "https://gist.githubusercontent.com/k-metrics/76fea197fa32466a2f99ff59f721b98a/raw/4c971a6cde2033e458525b793e832c4a87cbae2d/covid19_news.csv" %>% 
    readr::read_csv() %>% 
  dplyr::filter(area == "日本")
news

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	216672
Number of columns	21
_______________________
Column type frequency:
character	19
logical	1
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	15	0	213757	0
dateAnnounced	0	1.00	10	10	0	332	0
gender	105952	0.51	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	211502	0.02	8	23	0	8	0
notes	121960	0.44	1	270	0	85632	1
mhlwPatientNumber	216223	0.00	1	11	0	434	0
prefecturePatientNumber	99227	0.54	5	20	0	117436	0
prefectureSourceURL	185274	0.14	8	224	0	3454	0
residence	114042	0.47	1	38	0	1429	0
sourceURL	1362	0.99	1	239	0	10257	0
relatedPatients	204085	0.06	2	259	0	7470	0
knownCluster	214138	0.01	3	88	0	235	0
detectedCityTown	187639	0.13	2	22	0	667	0
cityPrefectureNumber	187940	0.13	1	34	0	28723	2
citySourceURL	204439	0.06	7	317	0	3695	0
deceasedDate	213661	0.01	10	10	0	282	0
deceasedReportedDate	215448	0.01	10	62	0	208	0
deathSourceURL	215593	0.00	14	123	0	658	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 213756, FAL: 2916

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	106065	0.51	38.01	20.69	0	20	30	50	100	▇▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient,
                # charterFlightPassenger, cruisePassengerDisembarked,
                ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	211914
Number of columns	16
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	15	0	211914	0
knownCluster	209430	0.01	3	88	0	232	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-12-25	2020-11-07	329
deceasedDate	211535	0	2020-02-13	2020-11-19	2020-05-08	150
deceasedReportedDate	211585	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	102799	0.51	FALSE	2	M: 61012, F: 48103
patientStatus	209403	0.01	FALSE	8	Hos: 1246, Dec: 371, Hom: 315, Dis: 276
ageBracket	102884	0.51	FALSE	12	20: 29297, 30: 18917, 40: 15988, 50: 14066
pcode	0	1.00	FALSE	47	13: 54970, 27: 28102, 14: 18726, 23: 15200
pref	0	1.00	FALSE	47	東京都: 54970, 大阪府: 28102, 神奈川: 18726, 愛知県: 15200
region	0	1.00	FALSE	8	関東地: 101886, 近畿地: 45778, 中部地: 23564, 九州地: 17694
広域圏	17809	0.92	FALSE	8	首都圏: 102395, 近畿圏: 44581, 中部圏: 21892, 九州圏: 12589
通俗的区分	0	1.00	FALSE	11	関東: 101886, 関西: 44581, 東海: 20805, 北海道: 12704
fct_pref	0	1.00	FALSE	47	Tok: 54970, Osa: 28102, Kan: 18726, Aic: 15200

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 211914
cluster	0	1	0.01	FAL: 209430, TRU: 2484

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.68）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.68）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

# 祝日ファイルは以下をダウンロードしておく（SSLがエラーになるので自動処理できない）
# "https://www8.cao.go.jp/chosei/shukujitsu/syukujitsu.csv"

# 祝日判定関数
source("https://raw.githubusercontent.com/logics-of-blue/website/master/010_forecast/20190714_R%E8%A8%80%E8%AA%9E%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8B%E6%97%A5%E6%9C%AC%E3%81%AE%E7%A5%9D%E6%97%A5%E5%88%A4%E5%AE%9A/jholiday.R", encoding="utf-8")

sec_scale <- 100

emergency <- news %>% 
  dplyr::filter(category == "緊急事態")

goto <- news %>% 
  dplyr::filter(category == "GoTo")

x_by_all %>% 
  dplyr::mutate(hday = is.jholiday(target_date = date,
                                   holiday_source = "./Covid19/syukujitsu.csv"),
                wday = lubridate::wday(date, week_start = 1),
                wday = dplyr::if_else(wday > 5, TRUE, FALSE),
                wday = dplyr::if_else(hday | wday, 3000L,  NA_integer_)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = wday), stat = "identity", width = 1.0,
                      fill = "red", alpha = 0.1) + 
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = emergency,
                        colour = "dark blue", linetype = "dashed", size = 0.15) +
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = goto,
                        colour = "magenta", linetype = "dashed", size = 0.25) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      fill = "dark gray", alpha = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
                              size = 2.0, data = emergency) +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2250, label = news),
                              size = 2.5, data = goto, colour = "magenta") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

死亡者の日次集計

厚生労働省のデータと乖離がある。 　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

sec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n), fill = "dark gray", stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), colour = "dark green",
                       linetype = "dashed", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale), colour = "dark green") +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・同移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計死亡者数（実線）")
    )

　

年代別

x_by_age <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9"))
x_by_age

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(rate = round((n / sum(n) * 100), 1))

　

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = ageBracket, values_from = n)

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() +
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) +
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

都道府県別

g_age_by_pref <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::group_by(pref, ageBracket, cluster) %>%
  # dplyr::group_by(pref, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") +
    # ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref

g_age_by_pref + 
  ggplot2::facet_grid(~ cluster)

g_age_by_pref_wo <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(pref, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref_wo

g_age_by_pref_wo + 
  ggplot2::facet_grid(~ cluster) + 
  ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))

　

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ymax <- 3000

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, ymax), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, ymax), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 95.81909 95.71150 93.14861 78.70304 95.50216 97.13371 87.45036 86.36398
##  [9] 86.81761 85.66410 80.33717 86.65715 87.35969 83.55417
## 
## $北海道地方$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 79.04350 70.16304
## 50.57143 76.21260 65.89051
## 50.71429 70.04700 57.81774
## 50.85714 54.51655 41.71299
## 51.00000 69.64911 55.96332
## 51.14286 70.14436 55.85706
## 51.28571 58.89188 43.77393
## 51.42857 54.04967 36.94350
## 51.57143 51.96070 33.50857
## 51.71429 48.27636 28.48448
## 51.85714 41.14425 20.39677
## 52.00000 45.57959 23.83444
## 52.14286 44.58660 21.94389
## 52.28571 39.00810 15.42683
## 
## $北海道地方$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 112.5947 121.4751
## 50.57143 115.2104 125.5325
## 50.71429 116.2502 128.4795
## 50.85714 102.8895 115.6931
## 51.00000 121.3552 135.0410
## 51.14286 124.1231 138.4104
## 51.28571 116.0088 131.1268
## 51.42857 118.6783 135.7845
## 51.57143 121.6745 140.1267
## 51.71429 123.0518 142.8437
## 51.85714 119.5301 140.2776
## 52.00000 127.7347 149.4799
## 52.14286 130.1328 152.7755
## 52.28571 128.1003 151.6815
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 87.72131 83.38672 83.62974 83.87277 84.11579 84.35882 84.60185 84.84487
##  [9] 85.08790 85.33092 85.57395 85.81697 86.06000 86.30302
## 
## $東北地方$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 76.82690 71.05974
## 50.57143 71.23708 64.80545
## 50.71429 71.16834 64.57167
## 50.85714 71.10721 64.34954
## 51.00000 71.05317 64.13823
## 51.14286 71.00573 63.93703
## 51.28571 70.96447 63.74529
## 51.42857 70.92903 63.56242
## 51.57143 70.89904 63.38792
## 51.71429 70.87421 63.22130
## 51.85714 70.85426 63.06213
## 52.00000 70.83892 62.91002
## 52.14286 70.82796 62.76461
## 52.28571 70.82117 62.62558
## 
## $東北地方$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%      95%
## 50.42857  98.61572 104.3829
## 50.57143  95.53636 101.9680
## 50.71429  96.09115 102.6878
## 50.85714  96.63833 103.3960
## 51.00000  97.17842 104.0934
## 51.14286  97.71191 104.7806
## 51.28571  98.23922 105.4584
## 51.42857  98.76072 106.1273
## 51.57143  99.27675 106.7879
## 51.71429  99.78763 107.4405
## 51.85714 100.29363 108.0858
## 52.00000 100.79502 108.7239
## 52.14286 101.29203 109.3554
## 52.28571 101.78487 109.9805
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 1898.333 1546.049 1241.407 1578.082 1853.805 2131.682 2106.975 2142.190
##  [9] 1846.735 1566.125 1898.934 2153.044 2402.738 2352.386
## 
## $関東地方$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 1814.831 1770.627
## 50.57143 1442.043 1386.986
## 50.71429 1128.562 1068.826
## 50.85714 1460.722 1398.595
## 51.00000 1733.145 1669.271
## 51.14286 2007.408 1941.621
## 51.28571 1977.856 1909.504
## 51.42857 1994.126 1915.746
## 51.57143 1682.960 1596.263
## 51.71429 1389.244 1295.608
## 51.85714 1710.846 1611.278
## 52.00000 1955.002 1850.165
## 52.14286 2195.475 2085.756
## 52.28571 2136.271 2021.867
## 
## $関東地方$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 1981.835 2026.039
## 50.57143 1650.054 1705.111
## 50.71429 1354.252 1413.988
## 50.85714 1695.443 1757.570
## 51.00000 1974.466 2038.340
## 51.14286 2255.957 2321.744
## 51.28571 2236.095 2304.447
## 51.42857 2290.254 2368.634
## 51.57143 2010.510 2097.207
## 51.71429 1743.007 1836.642
## 51.85714 2087.021 2186.589
## 52.00000 2351.086 2455.923
## 52.14286 2610.001 2719.719
## 52.28571 2568.500 2682.905
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 365.0563 268.1102 192.7982 306.5504 376.3878 407.1157 344.8529 369.3256
##  [9] 272.1387 196.5995 310.1374 379.7725 410.3095 347.8665
## 
## $中部地方$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 338.5223 324.4761
## 50.57143 235.8742 218.8096
## 50.71429 156.2260 136.8658
## 50.85714 266.5106 245.3147
## 51.00000 333.4957 310.7899
## 51.14286 361.8349 337.8647
## 51.28571 297.5466 272.5042
## 51.42857 315.6137 287.1804
## 51.57143 214.5300 184.0338
## 51.71429 135.7307 103.5087
## 51.85714 246.5062 212.8219
## 52.00000 313.7790 278.8441
## 52.14286 342.2816 306.2699
## 52.28571 278.0772 241.1330
## 
## $中部地方$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 391.5902 405.6365
## 50.57143 300.3461 317.4107
## 50.71429 229.3704 248.7305
## 50.85714 346.5903 367.7861
## 51.00000 419.2799 441.9857
## 51.14286 452.3965 476.3667
## 51.28571 392.1591 417.2016
## 51.42857 423.0375 451.4708
## 51.57143 329.7474 360.2436
## 51.71429 257.4684 289.6904
## 51.85714 373.7686 407.4529
## 52.00000 445.7660 480.7008
## 52.14286 478.3373 514.3491
## 52.28571 417.6559 454.6001
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 431.9616 372.9706 226.4985 464.5251 545.8991 521.0046 294.5726 410.7916
##  [9] 328.7025 183.7077 423.1623 505.9167 482.3566 257.2143
## 
## $近畿地方$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%       95%
## 50.42857 379.54126 351.79163
## 50.57143 311.67811 279.23180
## 50.71429 162.14189 128.07355
## 50.85714 397.43183 361.91481
## 51.00000 476.34600 439.52683
## 51.14286 449.22938 411.23385
## 51.28571 220.78139 181.71870
## 51.42857 323.17345 276.79118
## 51.57143 234.50080 184.63341
## 51.71429  85.72484  33.85584
## 51.85714 321.77382 268.10199
## 52.00000 401.44631 346.14305
## 52.14286 375.08663 318.30135
## 52.28571 147.39295  89.25706
## 
## $近畿地方$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 484.3819 512.1315
## 50.57143 434.2632 466.7095
## 50.71429 290.8551 324.9235
## 50.85714 531.6184 567.1354
## 51.00000 615.4521 652.2713
## 51.14286 592.7799 630.7754
## 51.28571 368.3638 407.4264
## 51.42857 498.4098 544.7921
## 51.57143 422.9043 472.7717
## 51.71429 281.6906 333.5596
## 51.85714 524.5509 578.2227
## 52.00000 610.3870 665.6903
## 52.14286 589.6265 646.4118
## 52.28571 367.0356 425.1715
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 179.1196 147.7509 161.3571 152.9183 146.0542 172.1134 158.7090 153.3913
##  [9] 173.3294 153.1205 160.8899 171.3069 153.0051 167.1401
## 
## $中国地方$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 165.8440 158.8164
## 50.57143 133.2671 125.5999
## 50.71429 146.6487 138.8626
## 50.85714 136.3919 127.6433
## 51.00000 128.9408 119.8815
## 51.14286 154.6130 145.3488
## 51.28571 139.6967 129.6322
## 51.42857 133.1242 122.3954
## 51.57143 152.0988 140.8600
## 51.71429 130.6082 118.6909
## 51.85714 137.7824 125.5500
## 52.00000 147.1877 134.4198
## 52.14286 127.9119 114.6284
## 52.28571 141.4811 127.8980
## 
## $中国地方$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 192.3951 199.4227
## 50.57143 162.2347 169.9020
## 50.71429 176.0654 183.8515
## 50.85714 169.4447 178.1933
## 51.00000 163.1676 172.2269
## 51.14286 189.6139 198.8781
## 51.28571 177.7213 187.7858
## 51.42857 173.6585 184.3873
## 51.57143 194.5600 205.7988
## 51.71429 175.6329 187.5502
## 51.85714 183.9975 196.2299
## 52.00000 195.4261 208.1941
## 52.14286 178.0983 191.3818
## 52.28571 192.7991 206.3821
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 26.20488 27.97786 27.97259 31.02162 33.71928 35.64143 33.67417 34.09888
##  [9] 34.50659 34.84844 35.10775 35.28720 35.39935 35.46024
## 
## $四国地方$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 21.00533 18.25286
## 50.57143 22.00033 18.83602
## 50.71429 21.78195 18.50482
## 50.85714 24.75980 21.44500
## 51.00000 27.41617 24.07951
## 51.14286 29.29185 25.93059
## 51.28571 27.25411 23.85554
## 51.42857 27.43581 23.90859
## 51.57143 27.61973 23.97404
## 51.71429 27.73741 23.97306
## 51.85714 27.76374 23.87605
## 52.00000 27.69941 23.68267
## 52.14286 27.55868 23.40808
## 52.28571 27.36095 23.07345
## 
## $四国地方$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 31.40443 34.15691
## 50.57143 33.95539 37.11971
## 50.71429 34.16324 37.44037
## 50.85714 37.28344 40.59825
## 51.00000 40.02238 43.35904
## 51.14286 41.99101 45.35227
## 51.28571 40.09423 43.49280
## 51.42857 40.76196 44.28917
## 51.57143 41.39346 45.03915
## 51.71429 41.95947 45.72382
## 51.85714 42.45176 46.33945
## 52.00000 42.87499 46.89172
## 52.14286 43.24002 47.39062
## 52.28571 43.55952 47.84703
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1]  82.62624 101.30365 205.34338 190.38263 142.67097 140.62786 107.28955
##  [8] 109.76637 109.64360 123.67364 122.79504 123.98746 129.11489  89.42613
## 
## $九州地方$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%       95%
## 50.42857  53.32084  37.80749
## 50.57143  65.69418  46.84366
## 50.71429 166.76810 146.34759
## 50.85714 151.69251 131.21120
## 51.00000 103.12906  82.19684
## 51.14286  97.07496  74.01944
## 51.28571  60.06509  35.06598
## 51.42857  57.33014  29.57208
## 51.57143  53.01383  23.03584
## 51.71429  63.43006  31.53904
## 51.85714  59.71079  26.31600
## 52.00000  57.95241  22.99557
## 52.14286  59.79652  23.10159
## 52.28571  16.97308 -21.38125
## 
## $九州地方$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 111.9316 127.4450
## 50.57143 136.9131 155.7637
## 50.71429 243.9187 264.3392
## 50.85714 229.0728 249.5541
## 51.00000 182.2129 203.1451
## 51.14286 184.1808 207.2363
## 51.28571 154.5140 179.5131
## 51.42857 162.2026 189.9607
## 51.57143 166.2734 196.2514
## 51.71429 183.9172 215.8082
## 51.85714 185.8793 219.2741
## 52.00000 190.0225 224.9794
## 52.14286 198.4333 235.1282
## 52.28571 161.8792 200.2335

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 95.81909 95.71150 93.14861 78.70304 95.50216 97.13371 87.45036 86.36398
##  [9] 86.81761 85.66410 80.33717 86.65715 87.35969 83.55417
## 
## $北海道$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 79.04350 70.16304
## 50.57143 76.21260 65.89051
## 50.71429 70.04700 57.81774
## 50.85714 54.51655 41.71299
## 51.00000 69.64911 55.96332
## 51.14286 70.14436 55.85706
## 51.28571 58.89188 43.77393
## 51.42857 54.04967 36.94350
## 51.57143 51.96070 33.50857
## 51.71429 48.27636 28.48448
## 51.85714 41.14425 20.39677
## 52.00000 45.57959 23.83444
## 52.14286 44.58660 21.94389
## 52.28571 39.00810 15.42683
## 
## $北海道$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 112.5947 121.4751
## 50.57143 115.2104 125.5325
## 50.71429 116.2502 128.4795
## 50.85714 102.8895 115.6931
## 51.00000 121.3552 135.0410
## 51.14286 124.1231 138.4104
## 51.28571 116.0088 131.1268
## 51.42857 118.6783 135.7845
## 51.57143 121.6745 140.1267
## 51.71429 123.0518 142.8437
## 51.85714 119.5301 140.2776
## 52.00000 127.7347 149.4799
## 52.14286 130.1328 152.7755
## 52.28571 128.1003 151.6815
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 2.517939 2.707801 2.586850 2.663901 2.614816 2.646086 2.626166 2.638856
##  [9] 2.630771 2.635921 2.632641 2.634731 2.633399 2.634247
## 
## $青森県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                 80%       95%
## 50.42857 -0.2951522 -1.784313
## 50.57143 -0.4207360 -2.076883
## 50.71429 -0.6480135 -2.360447
## 50.85714 -0.7762812 -2.597404
## 51.00000 -0.9538379 -2.842969
## 51.14286 -1.0853773 -3.060694
## 51.28571 -1.2370195 -3.282066
## 51.42857 -1.3664613 -3.486748
## 51.57143 -1.5026301 -3.690721
## 51.71429 -1.6273531 -3.884194
## 51.85714 -1.7531628 -4.074867
## 52.00000 -1.8724375 -4.258388
## 52.14286 -1.9906028 -4.438402
## 52.28571 -2.1045453 -4.613111
## 
## $青森県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 5.331030 6.820191
## 50.57143 5.836338 7.492485
## 50.71429 5.821714 7.534147
## 50.85714 6.104084 7.925207
## 51.00000 6.183470 8.072601
## 51.14286 6.377549 8.352866
## 51.28571 6.489351 8.534397
## 51.42857 6.644173 8.764459
## 51.57143 6.764173 8.952264
## 51.71429 6.899196 9.156037
## 51.85714 7.018444 9.340148
## 52.00000 7.141899 9.527849
## 52.14286 7.257401 9.705200
## 52.28571 7.373040 9.881605
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] -2.223092  5.653637  5.176716  4.648480  5.632262  5.359812  9.765933
##  [8]  9.047905  6.861020  7.328768  6.487146  6.804765  6.140057  4.721670
## 
## $岩手県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                 80%         95%
## 50.42857 -5.5897895 -7.37201147
## 50.57143  1.8742513 -0.12643447
## 50.71429  1.2742965 -0.79151990
## 50.85714  0.6946425 -1.39839267
## 51.00000  1.6489700 -0.45965720
## 51.14286  1.3549792 -0.76505101
## 51.28571  5.7427536  3.61301122
## 51.42857  4.8349339  2.60472179
## 51.57143  2.5704055  0.29909142
## 51.71429  2.9943268  0.69981219
## 51.85714  2.1209656 -0.19035056
## 52.00000  2.4117146  0.08617399
## 52.14286  1.7222625 -0.61637644
## 52.28571  0.2801283 -2.07108178
## 
## $岩手県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%       95%
## 50.42857  1.143605  2.925827
## 50.57143  9.433022 11.433708
## 50.71429  9.079136 11.144953
## 50.85714  8.602318 10.695353
## 51.00000  9.615553 11.724180
## 51.14286  9.364644 11.484674
## 51.28571 13.789112 15.918854
## 51.42857 13.260876 15.491088
## 51.57143 11.151634 13.422949
## 51.71429 11.663209 13.957724
## 51.85714 10.853326 13.164642
## 52.00000 11.197816 13.523357
## 52.14286 10.557851 12.896490
## 52.28571  9.163212 11.514422
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 45.45171 38.72460 36.05442 40.91065 46.87133 43.51916 42.79628 43.36348
##  [9] 41.99624 39.54324 42.10009 44.79718 42.20522 45.27566
## 
## $宮城県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 38.64813 35.04654
## 50.57143 31.67821 27.94808
## 50.71429 28.80612 24.96910
## 50.85714 33.56334 29.67391
## 51.00000 39.21726 35.16543
## 51.14286 35.80691 31.72430
## 51.28571 34.76874 30.51921
## 51.42857 34.93337 30.47074
## 51.57143 33.20882 28.55704
## 51.71429 30.60487 25.87318
## 51.85714 32.92550 28.06877
## 52.00000 35.40678 30.43580
## 52.14286 32.64856 27.58957
## 52.28571 35.46691 30.27448
## 
## $宮城県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 52.25528 55.85687
## 50.57143 45.77100 49.50113
## 50.71429 43.30273 47.13975
## 50.85714 48.25795 52.14738
## 51.00000 54.52541 58.57723
## 51.14286 51.23140 55.31401
## 51.28571 50.82383 55.07335
## 51.42857 51.79359 56.25622
## 51.57143 50.78366 55.43544
## 51.71429 48.48162 53.21331
## 51.85714 51.27467 56.13141
## 52.00000 54.18758 59.15856
## 52.14286 51.76189 56.82088
## 52.28571 55.08440 60.27684
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 1.7564248 2.4577791 1.2767119 1.2835870 1.0458138 1.0200669 0.9691019
##  [8] 0.9580851 0.9465337 0.9429901 0.9402525 0.9392244 0.9385541 0.9382699
## 
## $秋田県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                 80%        95%
## 50.42857  0.2676956 -0.5203901
## 50.57143  0.9550884  0.1596119
## 50.71429 -0.2681485 -1.0859482
## 50.85714 -0.2656715 -1.0857995
## 51.00000 -0.5080997 -1.3306918
## 51.14286 -0.5355019 -1.3589703
## 51.28571 -0.5879060 -1.4121362
## 51.42857 -0.5999510 -1.4247255
## 51.57143 -0.6124533 -1.4377312
## 51.71429 -0.6168675 -1.4426062
## 51.85714 -0.6204520 -1.4466391
## 52.00000 -0.6223085 -1.4489341
## 52.14286 -0.6238001 -1.4508605
## 52.28571 -0.6249008 -1.4523934
## 
## $秋田県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 3.245154 4.033240
## 50.57143 3.960470 4.755946
## 50.71429 2.821572 3.639372
## 50.85714 2.832846 3.652974
## 51.00000 2.599727 3.422319
## 51.14286 2.575636 3.399104
## 51.28571 2.526110 3.350340
## 51.42857 2.516121 3.340896
## 51.57143 2.505521 3.330799
## 51.71429 2.502848 3.328586
## 51.85714 2.500957 3.327144
## 52.00000 2.500757 3.327383
## 52.14286 2.500908 3.327969
## 52.28571 2.501441 3.328933
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 7.640824 9.782492 9.848730 8.105547 8.759602 9.106445 8.373242 8.509724
##  [9] 8.878728 8.604559 8.591406 8.792419 8.709809 8.647266
## 
## $山形県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 5.713957 4.693935
## 50.57143 7.791082 6.736894
## 50.71429 7.695661 6.555896
## 50.85714 5.734794 4.479793
## 51.00000 6.252697 4.925622
## 51.14286 6.561071 5.213631
## 51.28571 5.706981 4.295547
## 51.42857 5.769644 4.319134
## 51.57143 6.080079 4.598564
## 51.71429 5.732211 4.211682
## 51.85714 5.635772 4.071154
## 52.00000 5.774625 4.177101
## 52.14286 5.618935 3.982725
## 52.28571 5.482543 3.807240
## 
## $山形県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%      95%
## 50.42857  9.567691 10.58771
## 50.57143 11.773902 12.82809
## 50.71429 12.001799 13.14156
## 50.85714 10.476300 11.73130
## 51.00000 11.266507 12.59358
## 51.14286 11.651819 12.99926
## 51.28571 11.039503 12.45094
## 51.42857 11.249804 12.70031
## 51.57143 11.677377 13.15889
## 51.71429 11.476907 12.99744
## 51.85714 11.547040 13.11166
## 52.00000 11.810213 13.40774
## 52.14286 11.800684 13.43689
## 52.28571 11.811989 13.48729
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 20.43148 22.72110 23.29703 25.65780 24.86485 25.73538 25.07144 25.82296
##  [9] 25.26880 25.91906 25.45831 26.02240 25.64113 26.13190
## 
## $福島県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 16.67135 14.68086
## 50.57143 18.77611 16.68776
## 50.71429 19.32807 17.22704
## 50.85714 21.65849 19.54138
## 51.00000 20.73136 18.54322
## 51.14286 21.38267 19.07848
## 51.28571 20.58929 18.21659
## 51.42857 21.14448 18.66784
## 51.57143 20.46492 17.92190
## 51.71429 20.93668 18.29917
## 51.85714 20.35414 17.65215
## 52.00000 20.75401 17.96510
## 52.14286 20.25426 17.40262
## 52.28571 20.59248 17.66008
## 
## $福島県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 24.19161 26.18211
## 50.57143 26.66610 28.75445
## 50.71429 27.26598 29.36702
## 50.85714 29.65712 31.77423
## 51.00000 28.99835 31.18649
## 51.14286 30.08809 32.39227
## 51.28571 29.55359 31.92630
## 51.42857 30.50144 32.97808
## 51.57143 30.07269 32.61571
## 51.71429 30.90143 33.53894
## 51.85714 30.56248 33.26447
## 52.00000 31.29078 34.07969
## 52.14286 31.02801 33.87965
## 52.28571 31.67133 34.60372
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 27.37107 24.47656 26.32145 24.64848 26.16340 29.53958 25.63102 26.29894
##  [9] 26.26279 26.20449 26.46192 26.73660 29.46843 28.08228
## 
## $茨城県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%       95%
## 50.42857 19.64347 15.552724
## 50.57143 16.43237 12.174037
## 50.71429 17.97267 13.553087
## 50.85714 16.00583 11.430688
## 51.00000 17.23655 12.510960
## 51.14286 20.33731 15.465919
## 51.28571 16.16133 11.148378
## 51.42857 16.27194 10.963968
## 51.57143 15.91870 10.442867
## 51.71429 15.55274  9.914039
## 51.85714 15.51114  9.714153
## 52.00000 15.49476  9.543685
## 52.14286 17.94287 11.841601
## 52.28571 16.27981 10.031966
## 
## $茨城県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 35.09867 39.18941
## 50.57143 32.52075 36.77909
## 50.71429 34.67023 39.08981
## 50.85714 33.29113 37.86627
## 51.00000 35.09024 39.81583
## 51.14286 38.74185 43.61324
## 51.28571 35.10071 40.11366
## 51.42857 36.32593 41.63391
## 51.57143 36.60688 42.08272
## 51.71429 36.85624 42.49494
## 51.85714 37.41269 43.20968
## 52.00000 37.97844 43.92951
## 52.14286 40.99399 47.09526
## 52.28571 39.88475 46.13259
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 22.55263 23.31602 21.78014 30.58425 28.68194 27.25539 25.57132 24.61280
##  [9] 25.21450 24.00393 30.94322 29.44385 28.31946 26.99210
## 
## $栃木県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 18.58209 16.48020
## 50.57143 19.26462 17.11993
## 50.71429 17.64946 15.46281
## 50.85714 26.37578 24.14796
## 51.00000 24.39711 22.12886
## 51.14286 22.89553 20.58755
## 51.28571 21.13770 18.79068
## 51.42857 19.83091 17.29953
## 51.57143 20.32805 17.74133
## 51.71429 19.01513 16.37421
## 51.85714 25.85412 23.16011
## 52.00000 24.25638 21.51030
## 52.14286 23.03546 20.23828
## 52.28571 21.61330 18.76594
## 
## $栃木県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 26.52318 28.62507
## 50.57143 27.36743 29.51212
## 50.71429 25.91082 28.09747
## 50.85714 34.79271 37.02054
## 51.00000 32.96678 35.23503
## 51.14286 31.61526 33.92323
## 51.28571 30.00495 32.35197
## 51.42857 29.39470 31.92608
## 51.57143 30.10094 32.68767
## 51.71429 28.99273 31.63365
## 51.85714 36.03233 38.72634
## 52.00000 34.63132 37.37740
## 52.14286 33.60346 36.40064
## 52.28571 32.37090 35.21826
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 45.33633 38.14028 33.23946 32.98794 36.47879 39.42966 42.30284 39.12655
##  [9] 37.61568 35.31443 36.32684 35.53092 38.12202 39.54634
## 
## $群馬県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 39.30202 36.10765
## 50.57143 30.93080 27.11432
## 50.71429 25.63569 21.61050
## 50.85714 25.32674 21.27114
## 51.00000 28.53440 24.32889
## 51.14286 31.39751 27.14555
## 51.28571 34.04738 29.67720
## 51.42857 30.35113 25.70570
## 51.57143 28.38685 23.50140
## 51.71429 25.85212 20.84307
## 51.85714 26.66258 21.54663
## 52.00000 25.64898 20.41779
## 52.14286 28.04248 22.70670
## 52.28571 29.26117 23.81652
## 
## $群馬県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 51.37063 54.56500
## 50.57143 45.34977 49.16624
## 50.71429 40.84323 44.86843
## 50.85714 40.64915 44.70474
## 51.00000 44.42319 48.62870
## 51.14286 47.46180 51.71376
## 51.28571 50.55830 54.92847
## 51.42857 47.90197 52.54739
## 51.57143 46.84451 51.72996
## 51.71429 44.77674 49.78579
## 51.85714 45.99109 51.10704
## 52.00000 45.41286 50.64404
## 52.14286 48.20155 53.53733
## 52.28571 49.83152 55.27617
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 250.8456 214.2881 197.3824 229.0742 250.8354 256.6779 273.2082 256.4539
##  [9] 245.6682 239.4321 251.7292 259.8224 262.6146 270.5115
## 
## $埼玉県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 231.5396 221.3196
## 50.57143 193.5916 182.6355
## 50.71429 176.1023 164.8372
## 50.85714 207.2260 195.6603
## 51.00000 228.4336 216.5747
## 51.14286 233.7358 221.5910
## 51.28571 249.7382 237.3139
## 51.42857 228.9818 214.4389
## 51.57143 216.8953 201.6638
## 51.71429 209.7374 194.0179
## 51.85714 221.1404 204.9477
## 52.00000 228.3650 211.7124
## 52.14286 230.3119 213.2119
## 52.28571 237.3850 219.8489
## 
## $埼玉県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 270.1516 280.3715
## 50.57143 234.9846 245.9407
## 50.71429 218.6626 229.9276
## 50.85714 250.9224 262.4882
## 51.00000 273.2373 285.0961
## 51.14286 279.6201 291.7649
## 51.28571 296.6782 309.1024
## 51.42857 283.9261 298.4689
## 51.57143 274.4412 289.6726
## 51.71429 269.1269 284.8463
## 51.85714 282.3180 298.5108
## 52.00000 291.2799 307.9324
## 52.14286 294.9174 312.0174
## 52.28571 303.6379 321.1740
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 190.9818 182.9330 172.6865 196.1052 194.1027 231.6603 225.4735 211.5257
##  [9] 206.1305 202.1904 220.6171 216.8719 257.5294 249.0222
## 
## $千葉県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 176.6562 169.0726
## 50.57143 166.7878 158.2411
## 50.71429 155.4821 146.3747
## 50.85714 177.9031 168.2675
## 51.00000 174.9548 164.8185
## 51.14286 211.6112 200.9978
## 51.28571 204.5619 193.4920
## 51.42857 188.0872 175.6796
## 51.57143 181.2468 168.0742
## 51.71429 176.0919 162.2762
## 51.85714 193.3579 178.9277
## 52.00000 188.4994 173.4799
## 52.14286 228.0856 212.4991
## 52.28571 218.5449 202.4112
## 
## $千葉県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 205.3074 212.8910
## 50.57143 199.0781 207.6249
## 50.71429 189.8909 198.9984
## 50.85714 214.3073 223.9429
## 51.00000 213.2506 223.3869
## 51.14286 251.7094 262.3228
## 51.28571 246.3850 257.4549
## 51.42857 234.9643 247.3719
## 51.57143 231.0143 244.1869
## 51.71429 228.2890 242.1047
## 51.85714 247.8764 262.3065
## 52.00000 245.2444 260.2639
## 52.14286 286.9731 302.5597
## 52.28571 279.4995 295.6332
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1]  886.5707  697.3513  560.3396  681.5851  868.4094  977.1615  932.0748
##  [8]  965.9160  787.0624  653.4868  775.8713  963.0732 1071.9505 1026.9052
## 
## $東京都$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 831.7507 802.7307
## 50.57143 634.2778 600.8887
## 50.71429 493.0787 457.4729
## 50.85714 611.1917 573.9276
## 51.00000 795.2559 756.5307
## 51.14286 901.4227 861.3290
## 51.28571 853.8598 812.4552
## 51.42857 876.9333 829.8287
## 51.57143 692.3183 642.1639
## 51.71429 554.3291 501.8382
## 51.85714 672.7906 618.2230
## 52.00000 856.3079 799.7897
## 52.14286 961.6536 903.2659
## 52.28571 913.1960 853.0020
## 
## $東京都$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%       95%
## 50.42857  941.3907  970.4106
## 50.57143  760.4247  793.8138
## 50.71429  627.6005  663.2062
## 50.85714  751.9785  789.2425
## 51.00000  941.5629  980.2880
## 51.14286 1052.9003 1092.9940
## 51.28571 1010.2898 1051.6944
## 51.42857 1054.8988 1102.0034
## 51.57143  881.8064  931.9608
## 51.71429  752.6445  805.1354
## 51.85714  878.9520  933.5196
## 52.00000 1069.8385 1126.3566
## 52.14286 1182.2473 1240.6350
## 52.28571 1140.6144 1200.8084
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 439.4367 372.1454 300.7558 403.8743 460.7275 517.6938 512.2647 495.1322
##  [9] 441.7304 370.2849 468.8445 518.9792 572.6395 567.4806
## 
## $神奈川県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 413.4225 399.6513
## 50.57143 343.0169 327.5972
## 50.71429 268.6945 251.7222
## 50.85714 370.9345 353.4972
## 51.00000 426.8483 408.9137
## 51.14286 482.6933 464.1652
## 51.28571 475.5886 456.1734
## 51.42857 453.7019 431.7700
## 51.57143 397.4108 373.9495
## 51.71429 323.4648 298.6798
## 51.85714 420.3252 394.6407
## 52.00000 468.8827 442.3632
## 52.14286 520.9307 493.5578
## 52.28571 513.9782 485.6558
## 
## $神奈川県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 465.4510 479.2221
## 50.57143 401.2739 416.6936
## 50.71429 332.8171 349.7894
## 50.85714 436.8141 454.2513
## 51.00000 494.6067 512.5412
## 51.14286 552.6942 571.2223
## 51.28571 548.9408 568.3560
## 51.42857 536.5626 558.4945
## 51.57143 486.0499 509.5113
## 51.71429 417.1050 441.8900
## 51.85714 517.3637 543.0482
## 52.00000 569.0757 595.5952
## 52.14286 624.3483 651.7213
## 52.28571 620.9830 649.3054
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 9.087946 8.195313 8.912127 8.068578 8.247484 8.086067 8.231704 8.100305
##  [9] 8.218859 8.111894 8.208402 8.121329 8.199890 8.129009
## 
## $新潟県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 5.996294 4.359673
## 50.57143 4.904049 3.161759
## 50.71429 5.609406 3.861051
## 50.85714 4.701836 2.919590
## 51.00000 4.811640 2.992814
## 51.14286 4.634053 2.806667
## 51.28571 4.718801 2.859183
## 51.42857 4.568514 2.698897
## 51.57143 4.632504 2.734003
## 51.71429 4.504393 2.594697
## 51.85714 4.551276 2.615311
## 52.00000 4.441225 2.493096
## 52.14286 4.474071 2.501742
## 52.28571 4.378762 2.393501
## 
## $新潟県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 12.17960 13.81622
## 50.57143 11.48658 13.22887
## 50.71429 12.21485 13.96320
## 50.85714 11.43532 13.21757
## 51.00000 11.68333 13.50215
## 51.14286 11.53808 13.36547
## 51.28571 11.74461 13.60423
## 51.42857 11.63209 13.50171
## 51.57143 11.80521 13.70371
## 51.71429 11.71940 13.62909
## 51.85714 11.86553 13.80149
## 52.00000 11.80143 13.74956
## 52.14286 11.92571 13.89804
## 52.28571 11.87926 13.86452
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 7.384775 7.405540 7.369884 7.283700 7.152781 6.982778 6.779160 6.547178
##  [9] 6.291838 6.017875 5.729737 5.431570 5.127206 4.820161
## 
## $富山県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%        95%
## 50.42857 4.808202  3.4442463
## 50.57143 4.719582  3.2977213
## 50.71429 4.569420  3.0869438
## 50.85714 4.366885  2.8228157
## 51.00000 4.120569  2.5154124
## 51.14286 3.838408  2.1738798
## 51.28571 3.527660  1.8064205
## 51.42857 3.194909  1.4203255
## 51.57143 2.846096  1.0220298
## 51.71429 2.486547  0.6171741
## 51.85714 2.121013  0.2106692
## 52.00000 1.753702 -0.1932436
## 52.14286 1.388315 -0.5909342
## 52.28571 1.028078 -0.9793299
## 
## $富山県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%      95%
## 50.42857  9.961349 11.32530
## 50.57143 10.091498 11.51336
## 50.71429 10.170349 11.65282
## 50.85714 10.200515 11.74458
## 51.00000 10.184993 11.79015
## 51.14286 10.127147 11.79168
## 51.28571 10.030660 11.75190
## 51.42857  9.899447 11.67403
## 51.57143  9.737580 11.56165
## 51.71429  9.549204 11.41858
## 51.85714  9.338462 11.24880
## 52.00000  9.109437 11.05638
## 52.14286  8.866097 10.84535
## 52.28571  8.612245 10.61965
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 6.505381 8.537214 7.135015 7.135015 7.135015 7.135015 7.135015 7.135015
##  [9] 7.135015 7.135015 7.135015 7.135015 7.135015 7.135015
## 
## $石川県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%        95%
## 50.42857 2.814493  0.8606548
## 50.57143 4.716954  2.6946313
## 50.71429 3.225012  1.1551814
## 50.85714 2.993454  0.8010448
## 51.00000 2.774175  0.4656862
## 51.14286 2.565406  0.1464017
## 51.28571 2.365767 -0.1589197
## 51.42857 2.174156 -0.4519641
## 51.57143 1.989675 -0.7341032
## 51.71429 1.811584 -1.0064707
## 51.85714 1.639260 -1.2700167
## 52.00000 1.472178 -1.5255466
## 52.14286 1.309887 -1.7737501
## 52.28571 1.151996 -2.0152235
## 
## $石川県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 10.19627 12.15011
## 50.57143 12.35747 14.37980
## 50.71429 11.04502 13.11485
## 50.85714 11.27658 13.46898
## 51.00000 11.49585 13.80434
## 51.14286 11.70462 14.12363
## 51.28571 11.90426 14.42895
## 51.42857 12.09587 14.72199
## 51.57143 12.28035 15.00413
## 51.71429 12.45845 15.27650
## 51.85714 12.63077 15.54005
## 52.00000 12.79785 15.79558
## 52.14286 12.96014 16.04378
## 52.28571 13.11803 16.28525
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 2.413417 2.377824 2.603214 1.767201 1.983177 1.811729 1.496921 1.617902
##  [9] 1.419265 1.339167 1.358180 1.233788 1.224993 1.200622
## 
## $福井県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                 80%        95%
## 50.42857  0.4429100 -0.6002135
## 50.57143  0.2346682 -0.8998502
## 50.71429  0.2762374 -0.9555896
## 50.85714 -0.7759897 -2.1222733
## 51.00000 -0.6022674 -1.9709190
## 51.14286 -0.8528243 -2.2633535
## 51.28571 -1.2154568 -2.6513031
## 51.42857 -1.1121623 -2.5573709
## 51.57143 -1.3397845 -2.8003370
## 51.71429 -1.4318463 -2.8987321
## 51.85714 -1.4211123 -2.8923809
## 52.00000 -1.5546938 -3.0308268
## 52.14286 -1.5670975 -3.0451408
## 52.28571 -1.5950392 -3.0749729
## 
## $福井県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 4.383924 5.427048
## 50.57143 4.520981 5.655499
## 50.71429 4.930191 6.162018
## 50.85714 4.310391 5.656674
## 51.00000 4.568622 5.937273
## 51.14286 4.476282 5.886811
## 51.28571 4.209300 5.645146
## 51.42857 4.347966 5.793174
## 51.57143 4.178314 5.638866
## 51.71429 4.110180 5.577066
## 51.85714 4.137473 5.608741
## 52.00000 4.022270 5.498403
## 52.14286 4.017083 5.495126
## 52.28571 3.996283 5.476217
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 2.567795 2.792767 4.311665 3.614734 2.830943 2.923558 4.994443 5.354024
##  [9] 3.723399 2.677117 4.164922 5.242373 4.452330 2.777991
## 
## $山梨県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                  80%         95%
## 50.42857  0.16896644 -1.10089693
## 50.57143  0.10302410 -1.32083954
## 50.71429  1.46938453 -0.03522805
## 50.85714  0.74005308 -0.78171084
## 51.00000 -0.10453389 -1.65848167
## 51.14286 -0.04997278 -1.62406473
## 51.28571  1.99812683  0.41197296
## 51.42857  2.35384755  0.76565005
## 51.57143  0.71984987 -0.87013321
## 51.71429 -0.34268476 -1.94127141
## 51.85714  1.11605033 -0.49792487
## 52.00000  2.18293873  0.56337204
## 52.14286  1.35993959 -0.27707306
## 52.28571 -0.33323140 -1.98021323
## 
## $山梨県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 4.966624 6.236488
## 50.57143 5.482509 6.906373
## 50.71429 7.153946 8.658559
## 50.85714 6.489414 8.011178
## 51.00000 5.766421 7.320368
## 51.14286 5.897088 7.471180
## 51.28571 7.990759 9.576913
## 51.42857 8.354201 9.942398
## 51.57143 6.726949 8.316932
## 51.71429 5.696919 7.295506
## 51.85714 7.213794 8.827769
## 52.00000 8.301807 9.921374
## 52.14286 7.544721 9.181733
## 52.28571 5.889214 7.536196
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 11.98824 11.84429 12.64946 12.79963 12.31481 12.19434 11.52210 11.97930
##  [9] 11.97930 11.97930 11.97930 11.97930 11.97930 11.97930
## 
## $長野県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 8.127764 6.084150
## 50.57143 7.652319 5.433226
## 50.71429 8.150361 5.768683
## 50.85714 8.013066 5.479214
## 51.00000 7.257097 4.579705
## 51.14286 6.879288 4.065670
## 51.28571 5.961613 3.018067
## 51.42857 6.275123 3.255515
## 51.57143 6.086055 2.966361
## 51.71429 5.902868 2.686200
## 51.85714 5.725043 2.414241
## 52.00000 5.552137 2.149804
## 52.14286 5.383762 1.892297
## 52.28571 5.219580 1.641201
## 
## $長野県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 15.84872 17.89234
## 50.57143 16.03625 18.25535
## 50.71429 17.14856 19.53024
## 50.85714 17.58619 20.12004
## 51.00000 17.37252 20.04992
## 51.14286 17.50939 20.32301
## 51.28571 17.08259 20.02614
## 51.42857 17.68347 20.70308
## 51.57143 17.87254 20.99223
## 51.71429 18.05573 21.27240
## 51.85714 18.23355 21.54435
## 52.00000 18.40646 21.80879
## 52.14286 18.57483 22.06630
## 52.28571 18.73902 22.31739
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 38.04806 29.47264 23.43415 30.20889 36.95870 34.75646 31.29335 29.53148
##  [9] 23.91506 26.97349 29.00739 36.63486 35.75421 19.03932
## 
## $岐阜県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%       95%
## 50.42857 31.39400 27.871547
## 50.57143 22.66505 19.061325
## 50.71429 16.47642 12.793217
## 50.85714 23.10419 19.343187
## 51.00000 29.71001 25.872780
## 51.14286 27.36658 23.454618
## 51.28571 23.76493 19.779632
## 51.42857 21.57169 17.358032
## 51.57143 15.77877 11.471684
## 51.71429 18.66446 14.265928
## 51.85714 20.52914 16.041025
## 52.00000 27.99070 23.414754
## 52.14286 26.94726 22.285144
## 52.28571 10.07254  5.325816
## 
## $岐阜県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 44.70212 48.22457
## 50.57143 36.28023 39.88395
## 50.71429 30.39189 34.07509
## 50.85714 37.31360 41.07460
## 51.00000 44.20739 48.04462
## 51.14286 42.14634 46.05831
## 51.28571 38.82177 42.80707
## 51.42857 37.49127 41.70493
## 51.57143 32.05134 36.35843
## 51.71429 35.28251 39.68104
## 51.85714 37.48564 41.97375
## 52.00000 45.27902 49.85496
## 52.14286 44.56116 49.22327
## 52.28571 28.00610 32.75283
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 30.30598 25.41831 22.72420 25.41144 30.55783 32.83393 25.91979 28.73582
##  [9] 24.92939 22.85534 24.92411 28.88604 30.63828 25.31546
## 
## $静岡県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%       95%
## 50.42857 22.802465 18.830342
## 50.57143 16.739111 12.144622
## 50.71429 13.503133  8.621796
## 50.85714 15.678630 10.526392
## 51.00000 20.338866 14.929275
## 51.14286 22.150914 16.495669
## 51.28571 14.792067  8.901404
## 51.42857 16.523762 10.059089
## 51.57143 12.006026  5.164809
## 51.71429  9.353493  2.206047
## 51.85714 10.867565  3.426482
## 52.00000 14.295874  6.572310
## 52.14286 15.533340  7.537267
## 52.28571  9.712713  1.453117
## 
## $静岡県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 37.80950 41.78162
## 50.57143 34.09750 38.69199
## 50.71429 31.94526 36.82660
## 50.85714 35.14425 40.29649
## 51.00000 40.77679 46.18638
## 51.14286 43.51694 49.17218
## 51.28571 37.04752 42.93818
## 51.42857 40.94787 47.41255
## 51.57143 37.85276 44.69398
## 51.71429 36.35719 43.50463
## 51.85714 38.98065 46.42173
## 52.00000 43.47620 51.19976
## 52.14286 45.74323 53.73930
## 52.28571 40.91821 49.17780
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 263.6141 198.5699 135.0745 221.2749 262.0204 282.2274 272.2241 271.6060
##  [9] 212.5869 150.3923 233.9997 270.0261 282.9135 271.3117
## 
## $愛知県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%       95%
## 50.42857 244.5090 234.39539
## 50.57143 176.0386 164.11121
## 50.71429 109.8905  96.55894
## 50.85714 193.9522 179.48843
## 51.00000 232.9315 217.53269
## 51.14286 251.6567 235.47352
## 51.28571 240.3963 223.54776
## 51.42857 233.5971 213.47633
## 51.57143 171.4892 149.73335
## 51.71429 106.7366  83.62667
## 51.85714 188.1939 163.94582
## 52.00000 222.3939 197.17888
## 52.14286 233.7175 207.67472
## 52.28571 220.7687 194.01292
## 
## $愛知県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 282.7192 292.8328
## 50.57143 221.1012 233.0286
## 50.71429 160.2584 173.5900
## 50.85714 248.5976 263.0614
## 51.00000 291.1094 306.5082
## 51.14286 312.7982 328.9814
## 51.28571 304.0518 320.9004
## 51.42857 309.6150 329.7357
## 51.57143 253.6847 275.4405
## 51.71429 194.0480 217.1579
## 51.85714 279.8054 304.0535
## 52.00000 317.6583 342.8732
## 52.14286 332.1095 358.1523
## 52.28571 321.8546 348.6105
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 11.29652 12.30615 11.10763 11.54644 12.65323 11.26379 11.29733 11.58408
##  [9] 12.02648 11.27137 11.64022 12.08578 11.79357 11.96690
## 
## $三重県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 6.977700 4.691457
## 50.57143 7.604368 5.115392
## 50.71429 6.136523 3.504979
## 50.85714 6.377180 3.640741
## 51.00000 7.332711 4.516201
## 51.14286 5.824022 2.944381
## 51.28571 5.760735 2.829839
## 51.42857 5.689889 2.569692
## 51.57143 5.980778 2.780379
## 51.71429 5.101543 1.835434
## 51.85714 5.365938 2.044532
## 52.00000 5.721445 2.352369
## 52.14286 5.349908 1.938841
## 52.28571 5.452030 2.003266
## 
## $三重県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 15.61533 17.90158
## 50.57143 17.00794 19.49692
## 50.71429 16.07873 18.71028
## 50.85714 16.71569 19.45213
## 51.00000 17.97374 20.79025
## 51.14286 16.70356 19.58320
## 51.28571 16.83393 19.76482
## 51.42857 17.47828 20.59847
## 51.57143 18.07217 21.27257
## 51.71429 17.44120 20.70730
## 51.85714 17.91451 21.23592
## 52.00000 18.45012 21.81920
## 52.14286 18.23722 21.64829
## 52.28571 18.48177 21.93053
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 11.185420  6.058710  9.729214  8.089295  9.295136  8.771581  9.168214
##  [8]  9.001440  9.132078  9.079092  9.122182  9.105398  9.119633  9.114336
## 
## $滋賀県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%        95%
## 50.42857 7.128935  4.9815621
## 50.57143 1.946141 -0.2309212
## 50.71429 5.173818  2.7623362
## 50.85714 3.456808  1.0045179
## 51.00000 4.498168  1.9588067
## 51.14286 3.895435  1.3141592
## 51.28571 4.190240  1.5550596
## 51.42857 3.946484  1.2705518
## 51.57143 3.994391  1.2746637
## 51.71429 3.867019  1.1079151
## 51.85714 3.834730  1.0357224
## 52.00000 3.745870  0.9087065
## 52.14286 3.688378  0.8132453
## 52.28571 3.613015  0.7007910
## 
## $滋賀県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 15.24190 17.38928
## 50.57143 10.17128 12.34834
## 50.71429 14.28461 16.69609
## 50.85714 12.72178 15.17407
## 51.00000 14.09210 16.63146
## 51.14286 13.64773 16.22900
## 51.28571 14.14619 16.78137
## 51.42857 14.05640 16.73233
## 51.57143 14.26977 16.98949
## 51.71429 14.29116 17.05027
## 51.85714 14.40963 17.20864
## 52.00000 14.46493 17.30209
## 52.14286 14.55089 17.42602
## 52.28571 14.61566 17.52788
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 45.35318 63.05540 53.57147 58.03751 61.01464 67.52736 37.77976 49.42201
##  [9] 54.13460 51.53681 53.25576 53.22671 56.38982 43.39855
## 
## $京都府$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 34.72724 29.10221
## 50.57143 51.49592 45.37671
## 50.71429 41.84273 35.63391
## 50.85714 45.99450 39.61932
## 51.00000 48.53635 41.93074
## 51.14286 54.64504 47.82556
## 51.28571 24.52957 17.51534
## 51.42857 34.86517 27.15924
## 51.57143 38.93558 30.88971
## 51.71429 35.90178 27.62509
## 51.85714 37.14893 28.62248
## 52.00000 36.62669 27.83916
## 52.14286 39.31503 30.27619
## 52.28571 25.86809 16.58803
## 
## $京都府$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 55.97912 61.60415
## 50.57143 74.61487 80.73408
## 50.71429 65.30020 71.50902
## 50.85714 70.08051 76.45569
## 51.00000 73.49293 80.09854
## 51.14286 80.40968 87.22916
## 51.28571 51.02995 58.04418
## 51.42857 63.97886 71.68479
## 51.57143 69.33362 77.37950
## 51.71429 67.17183 75.44852
## 51.85714 69.36260 77.88905
## 52.00000 69.82673 78.61425
## 52.14286 73.46461 82.50346
## 52.28571 60.92901 70.20907
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 129.55326 135.68883  77.26241 157.04138 184.10312 163.80752   8.15799
##  [8]  72.22135  68.25038  27.53736  87.92852  99.57575  85.74151 -59.10167
## 
## $大阪府$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                  80%         95%
## 50.42857   89.629997   68.495901
## 50.57143   89.649613   65.277925
## 50.71429   29.218980    3.786322
## 50.85714  107.074048   80.622943
## 51.00000  132.283274  104.851506
## 51.14286  110.199137   81.820575
## 51.28571  -47.181153  -76.475925
## 51.42857    7.265020  -27.120784
## 51.57143   -1.488211  -38.405593
## 51.71429  -45.210705  -83.721205
## 51.85714   12.290624  -27.749657
## 52.00000   21.154451  -20.359277
## 52.14286    4.632267  -38.304374
## 52.28571 -142.812594 -187.126482
## 
## $大阪府$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%       95%
## 50.42857 169.47652 190.61061
## 50.57143 181.72804 206.09973
## 50.71429 125.30585 150.73851
## 50.85714 207.00870 233.45981
## 51.00000 235.92296 263.35473
## 51.14286 217.41590 245.79446
## 51.28571  63.49713  92.79191
## 51.42857 137.17768 171.56349
## 51.57143 137.98898 174.90636
## 51.71429 100.28543 138.79593
## 51.85714 163.56641 203.60669
## 52.00000 177.99705 219.51078
## 52.14286 166.85076 209.78740
## 52.28571  24.60926  68.92315
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 172.4221 154.9307 113.6785 207.4471 195.1585 190.3648 226.6084 195.5226
##  [9] 181.3299 147.8572 223.9425 213.9713 210.0817 239.4903
## 
## $兵庫県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%       95%
## 50.42857 155.73412 146.90006
## 50.57143 137.34091 128.02942
## 50.71429  95.23083  85.46521
## 50.85714 188.17971 177.98017
## 51.00000 175.10485 164.48911
## 51.14286 169.55466 158.53843
## 51.28571 205.06824 193.66559
## 51.42857 169.42548 155.61048
## 51.57143 153.84704 139.29848
## 51.71429 119.05519 103.80834
## 51.85714 193.87924 177.96470
## 52.00000 182.69758 166.14226
## 52.14286 177.64261 160.47040
## 52.28571 205.92633 188.15864
## 
## $兵庫県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 189.1100 197.9441
## 50.57143 172.5206 181.8321
## 50.71429 132.1262 141.8918
## 50.85714 226.7145 236.9140
## 51.00000 215.2121 225.8278
## 51.14286 211.1750 222.1912
## 51.28571 248.1485 259.5512
## 51.42857 221.6198 235.4348
## 51.57143 208.8128 223.3613
## 51.71429 176.6592 191.9060
## 51.85714 254.0058 269.9204
## 52.00000 245.2451 261.8004
## 52.14286 242.5208 259.6930
## 52.28571 273.0543 290.8220
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 29.78116 28.25368 30.24288 31.21364 31.70800 31.55343 30.94769 30.79249
##  [9] 30.90065 31.25603 31.55116 31.66245 31.66060 31.63492
## 
## $奈良県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 25.14490 22.69061
## 50.57143 23.29735 20.67362
## 50.71429 24.90529 22.07975
## 50.85714 25.83661 22.99019
## 51.00000 26.30329 23.44220
## 51.14286 26.01527 23.08355
## 51.28571 25.21705 22.18342
## 51.42857 24.79326 21.61745
## 51.57143 24.70114 21.41932
## 51.71429 24.91233 21.55419
## 51.85714 25.09073 21.67078
## 52.00000 25.08553 21.60392
## 52.14286 24.94676 21.39267
## 52.28571 24.77186 21.13877
## 
## $奈良県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 34.41742 36.87171
## 50.57143 33.21002 35.83375
## 50.71429 35.58047 38.40602
## 50.85714 36.59067 39.43710
## 51.00000 37.11272 39.97380
## 51.14286 37.09158 40.02330
## 51.28571 36.67834 39.71196
## 51.42857 36.79173 39.96753
## 51.57143 37.10016 40.38198
## 51.71429 37.59972 40.95787
## 51.85714 38.01159 41.43154
## 52.00000 38.23937 41.72098
## 52.14286 38.37444 41.92853
## 52.28571 38.49798 42.13106
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 2.114376 2.240756 2.240756 2.240756 2.240756 2.240756 2.240756 2.240756
##  [9] 2.240756 2.240756 2.240756 2.240756 2.240756 2.240756
## 
## $和歌山県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                 80%       95%
## 50.42857 -0.1798202 -1.394294
## 50.57143 -0.2354648 -1.546297
## 50.71429 -0.3335325 -1.696279
## 50.85714 -0.4279991 -1.840753
## 51.00000 -0.5192342 -1.980285
## 51.14286 -0.6075484 -2.115350
## 51.28571 -0.6932055 -2.246351
## 51.42857 -0.7764318 -2.373635
## 51.57143 -0.8574232 -2.497500
## 51.71429 -0.9363507 -2.618209
## 51.85714 -1.0133643 -2.735992
## 52.00000 -1.0885970 -2.851050
## 52.14286 -1.1621668 -2.963565
## 52.28571 -1.2341793 -3.073699
## 
## $和歌山県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 4.408573 5.623047
## 50.57143 4.716977 6.027809
## 50.71429 4.815045 6.177791
## 50.85714 4.909511 6.322265
## 51.00000 5.000746 6.461797
## 51.14286 5.089060 6.596862
## 51.28571 5.174718 6.727863
## 51.42857 5.257944 6.855147
## 51.57143 5.338935 6.979012
## 51.71429 5.417863 7.099721
## 51.85714 5.494876 7.217504
## 52.00000 5.570109 7.332562
## 52.14286 5.643679 7.445077
## 52.28571 5.715691 7.555211
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 0.9144056 0.8235787 0.7619317 0.7200900 0.6916908 0.6724153 0.6593325
##  [8] 0.6504528 0.6444258 0.6403352 0.6375587 0.6356742 0.6343952 0.6335271
## 
## $鳥取県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                  80%        95%
## 50.42857 -0.09280803 -0.6259947
## 50.57143 -0.20477980 -0.7491599
## 50.71429 -0.27748623 -0.8277208
## 50.85714 -0.32545764 -0.8789371
## 51.00000 -0.35748192 -0.9128804
## 51.14286 -0.37906040 -0.9356780
## 51.28571 -0.39372075 -0.9511735
## 51.42857 -0.40376406 -0.9618327
## 51.57143 -0.41070961 -0.9692646
## 51.71429 -0.41556912 -0.9745311
## 51.85714 -0.41902032 -0.9783395
## 52.00000 -0.42151910 -0.9811634
## 52.14286 -0.42337299 -0.9833216
## 52.28571 -0.42478985 -0.9850290
## 
## $鳥取県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 1.921619 2.454806
## 50.57143 1.851937 2.396317
## 50.71429 1.801350 2.351584
## 50.85714 1.765638 2.319117
## 51.00000 1.740863 2.296262
## 51.14286 1.723891 2.280509
## 51.28571 1.712386 2.269838
## 51.42857 1.704670 2.262738
## 51.57143 1.699561 2.258116
## 51.71429 1.696239 2.255201
## 51.85714 1.694138 2.253457
## 52.00000 1.692868 2.252512
## 52.14286 1.692163 2.252112
## 52.28571 1.691844 2.252083
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 0.5491329 0.5491329 0.5491329 0.5491329 0.5491329 0.5491329 0.5491329
##  [8] 0.5491329 0.5491329 0.5491329 0.5491329 0.5491329 0.5491329 0.5491329
## 
## $島根県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%      95%
## 50.42857 -5.866296 -9.26242
## 50.57143 -5.866296 -9.26242
## 50.71429 -5.866296 -9.26242
## 50.85714 -5.866296 -9.26242
## 51.00000 -5.866296 -9.26242
## 51.14286 -5.866296 -9.26242
## 51.28571 -5.866296 -9.26242
## 51.42857 -5.866296 -9.26242
## 51.57143 -5.866296 -9.26242
## 51.71429 -5.866296 -9.26242
## 51.85714 -5.866296 -9.26242
## 52.00000 -5.866296 -9.26242
## 52.14286 -5.866296 -9.26242
## 52.28571 -5.866296 -9.26242
## 
## $島根県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 6.964562 10.36069
## 50.57143 6.964562 10.36069
## 50.71429 6.964562 10.36069
## 50.85714 6.964562 10.36069
## 51.00000 6.964562 10.36069
## 51.14286 6.964562 10.36069
## 51.28571 6.964562 10.36069
## 51.42857 6.964562 10.36069
## 51.57143 6.964562 10.36069
## 51.71429 6.964562 10.36069
## 51.85714 6.964562 10.36069
## 52.00000 6.964562 10.36069
## 52.14286 6.964562 10.36069
## 52.28571 6.964562 10.36069
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 67.20619 70.86629 37.43426 26.23395 32.66851 36.22708 35.45343 48.22776
##  [9] 33.41151 40.99245 39.83319 36.85298 41.45735 37.17798
## 
## $岡山県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 60.56643 57.05156
## 50.57143 63.31366 59.31554
## 50.71429 29.87622 25.87524
## 50.85714 18.56896 14.51137
## 51.00000 24.76097 20.57498
## 51.14286 28.30509 24.11144
## 51.28571 27.28119 22.95506
## 51.42857 38.71037 33.67217
## 51.57143 23.22555 17.83343
## 51.71429 30.69175 25.23889
## 51.85714 29.35733 23.81174
## 52.00000 26.04447 20.32278
## 52.14286 30.55217 24.77931
## 52.28571 25.96853 20.03461
## 
## $岡山県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 73.84594 77.36082
## 50.57143 78.41892 82.41703
## 50.71429 44.99229 48.99328
## 50.85714 33.89893 37.95653
## 51.00000 40.57605 44.76205
## 51.14286 44.14907 48.34271
## 51.28571 43.62567 47.95179
## 51.42857 57.74514 62.78334
## 51.57143 43.59747 48.98959
## 51.71429 51.29315 56.74601
## 51.85714 50.30905 55.85463
## 52.00000 47.66150 53.38319
## 52.14286 52.36253 58.13539
## 52.28571 48.38742 54.32134
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 121.10642  95.54168 132.87294 112.07014 122.04355 150.77978 121.45521
##  [8] 136.64074 146.70169 134.07723 149.64430 165.45897 153.15645 175.81111
## 
## $広島県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%       95%
## 50.42857 113.28583 109.14586
## 50.57143  86.54074  81.77594
## 50.71429 122.01403 116.26568
## 50.85714  97.88101  90.36974
## 51.00000 105.92550  97.39312
## 51.14286 131.07419 120.64268
## 51.28571  97.71696  85.15069
## 51.42857 109.02214  94.40174
## 51.57143 114.26047  97.08713
## 51.71429  96.97104  77.32822
## 51.85714 107.86590  85.74975
## 52.00000 118.07331  92.98886
## 52.14286 100.45088  72.55024
## 52.28571 117.48811  86.61378
## 
## $広島県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 128.9270 133.0670
## 50.57143 104.5426 109.3074
## 50.71429 143.7318 149.4802
## 50.85714 126.2593 133.7705
## 51.00000 138.1616 146.6940
## 51.14286 170.4854 180.9169
## 51.28571 145.1935 157.7597
## 51.42857 164.2593 178.8797
## 51.57143 179.1429 196.3163
## 51.71429 171.1834 190.8262
## 51.85714 191.4227 213.5389
## 52.00000 212.8446 237.9291
## 52.14286 205.8620 233.7626
## 52.28571 234.1341 265.0084
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 7.734425 7.348746 6.639678 6.380620 7.361247 6.500093 6.779025 6.215908
##  [9] 6.130104 5.774958 5.626346 6.395627 5.731448 5.702025
## 
## $山口県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%          95%
## 50.42857 5.028411  3.595933674
## 50.57143 4.424529  2.876542059
## 50.71429 3.557755  1.926284358
## 50.85714 3.181308  1.487694381
## 51.00000 4.072534  2.331593637
## 51.14286 3.141952  1.364260198
## 51.28571 3.366007  1.559265273
## 51.42857 2.680863  0.809522951
## 51.57143 2.535426  0.632518907
## 51.71429 2.132007  0.203545279
## 51.85714 1.943675 -0.005812818
## 52.00000 2.679755  0.712690493
## 52.14286 1.987394  0.005411135
## 52.28571 1.933694 -0.061139431
## 
## $山口県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%      95%
## 50.42857 10.440438 11.87292
## 50.57143 10.272963 11.82095
## 50.71429  9.721600 11.35307
## 50.85714  9.579932 11.27355
## 51.00000 10.649961 12.39090
## 51.14286  9.858234 11.63593
## 51.28571 10.192042 11.99878
## 51.42857  9.750953 11.62229
## 51.57143  9.724782 11.62769
## 51.71429  9.417909 11.34637
## 51.85714  9.309017 11.25850
## 52.00000 10.111500 12.07856
## 52.14286  9.475502 11.45748
## 52.28571  9.470356 11.46519
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 0.7740799 1.0542821 0.8495209 0.7668730 0.8507576 0.8159573 0.5809936
##  [8] 0.5755316 0.4903475 0.6364970 0.7312391 0.5252878 0.6434144 0.7956369
## 
## $徳島県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                 80%       95%
## 50.42857 -0.8817850 -1.758347
## 50.57143 -0.6846667 -1.605211
## 50.71429 -0.9068401 -1.836601
## 50.85714 -1.0067293 -1.945618
## 51.00000 -0.9399199 -1.887847
## 51.14286 -0.9916342 -1.948515
## 51.28571 -1.2433550 -2.209107
## 51.42857 -1.3343675 -2.345407
## 51.57143 -1.4511509 -2.478918
## 51.71429 -1.3271584 -2.366655
## 51.85714 -1.2543261 -2.305421
## 52.00000 -1.4819481 -2.544515
## 52.14286 -1.3852607 -2.459176
## 52.28571 -1.2542531 -2.339399
## 
## $徳島県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 2.429945 3.306507
## 50.57143 2.793231 3.713775
## 50.71429 2.605882 3.535643
## 50.85714 2.540475 3.479364
## 51.00000 2.641435 3.589362
## 51.14286 2.623549 3.580430
## 51.28571 2.405342 3.371094
## 51.42857 2.485431 3.496470
## 51.57143 2.431846 3.459613
## 51.71429 2.600152 3.639649
## 51.85714 2.716804 3.767899
## 52.00000 2.532524 3.595090
## 52.14286 2.672090 3.746005
## 52.28571 2.845527 3.930673
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 2.664184 5.026419 5.041515 5.056610 5.071706 5.086801 5.101897 5.116992
##  [9] 5.132088 5.147183 5.162279 5.177374 5.192470 5.207565
## 
## $香川県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%        95%
## 50.42857 0.1358172 -1.2026195
## 50.57143 2.1530947  0.6320485
## 50.71429 2.1584327  0.6322212
## 50.85714 2.1638036  0.6324442
## 51.00000 2.1692071  0.6327171
## 51.14286 2.1746429  0.6330393
## 51.28571 2.1801105  0.6334103
## 51.42857 2.1856098  0.6338297
## 51.57143 2.1911405  0.6342970
## 51.71429 2.1967021  0.6348117
## 51.85714 2.2022944  0.6353733
## 52.00000 2.2079171  0.6359815
## 52.14286 2.2135700  0.6366358
## 52.28571 2.2192527  0.6373356
## 
## $香川県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 5.192551 6.530988
## 50.57143 7.899744 9.420791
## 50.71429 7.924597 9.450809
## 50.85714 7.949417 9.480777
## 51.00000 7.974205 9.510695
## 51.14286 7.998960 9.540564
## 51.28571 8.023683 9.570383
## 51.42857 8.048375 9.600155
## 51.57143 8.073035 9.629879
## 51.71429 8.097664 9.659555
## 51.85714 8.122263 9.689184
## 52.00000 8.146831 9.718767
## 52.14286 8.171369 9.748304
## 52.28571 8.195878 9.777795
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 3.012956 1.798975 2.920632 1.543031 2.442125 1.307580 2.757116 1.827759
##  [9] 2.517530 1.891006 2.460082 1.943186 2.412686 1.986236
## 
## $愛媛県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                 80%        95%
## 50.42857  0.6424172 -0.6124704
## 50.57143 -0.9744395 -2.4425965
## 50.71429 -0.2993403 -2.0038905
## 50.85714 -1.9914792 -3.8625360
## 51.00000 -1.4457933 -3.5039327
## 51.14286 -2.8498206 -5.0506156
## 51.28571 -1.6997426 -4.0590612
## 51.42857 -2.7948203 -5.2418657
## 51.57143 -2.3236334 -4.8863903
## 51.71429 -3.1260254 -5.7818814
## 51.85714 -2.7559791 -5.5171950
## 52.00000 -3.4389877 -6.2881385
## 52.14286 -3.1531315 -6.0994972
## 52.28571 -3.7374919 -6.7674505
## 
## $愛媛県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%       95%
## 50.42857 5.383496  6.638383
## 50.57143 4.572390  6.040547
## 50.71429 6.140604  7.845154
## 50.85714 5.077542  6.948599
## 51.00000 6.330042  8.388182
## 51.14286 5.464981  7.665776
## 51.28571 7.213975  9.573294
## 51.42857 6.450338  8.897383
## 51.57143 7.358693  9.921450
## 51.71429 6.908037  9.563893
## 51.85714 7.676143 10.437358
## 52.00000 7.325360 10.174511
## 52.14286 7.978503 10.924869
## 52.28571 7.709964 10.739923
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 16.97975 23.28649 20.25336 22.77390 22.15099 19.64663 22.84001 20.06387
##  [9] 22.16571 21.02996 21.12951 21.90247 20.57781 22.07280
## 
## $高知県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 14.01606 12.44718
## 50.57143 20.05964 18.35144
## 50.71429 16.93238 15.17436
## 50.85714 19.18309 17.28222
## 51.00000 18.46421 16.51255
## 51.14286 15.76608 13.71185
## 51.28571 18.82175 16.69461
## 51.42857 15.65574 13.32222
## 51.57143 17.49233 15.01840
## 51.71429 16.22217 13.67709
## 51.85714 16.06613 13.38573
## 52.00000 16.69949 13.94520
## 52.14286 15.17689 12.31781
## 52.28571 16.50192 13.55287
## 
## $高知県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 19.94344 21.51232
## 50.57143 26.51334 28.22154
## 50.71429 23.57434 25.33236
## 50.85714 26.36472 28.26559
## 51.00000 25.83778 27.78944
## 51.14286 23.52718 25.58141
## 51.28571 26.85827 28.98541
## 51.42857 24.47200 26.80552
## 51.57143 26.83908 29.31302
## 51.71429 25.83775 28.38283
## 51.85714 26.19289 28.87329
## 52.00000 27.10545 29.85974
## 52.14286 25.97873 28.83781
## 52.28571 27.64369 30.59274
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 53.74901 89.07614 76.32797 74.45751 87.18319 88.51090 44.87916 59.64866
##  [9] 72.13301 68.47122 68.47122 68.47122 68.47122 68.47122
## 
## $福岡県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%       95%
## 50.42857 34.86246 24.864520
## 50.57143 67.04798 55.386981
## 50.71429 53.66169 41.662881
## 50.85714 50.68608 38.102246
## 51.00000 62.35576 49.212913
## 51.14286 62.67059 48.991559
## 51.28571 18.06419  3.869212
## 51.42857 30.06629 14.406330
## 51.57143 40.78806 24.195042
## 51.71429 35.94911 18.732945
## 51.85714 34.67690 16.787271
## 52.00000 33.45088 14.912232
## 52.14286 32.26635 13.100652
## 52.28571 31.11937 11.346494
## 
## $福岡県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%      95%
## 50.42857  72.63557  82.6335
## 50.57143 111.10429 122.7653
## 50.71429  98.99425 110.9931
## 50.85714  98.22894 110.8128
## 51.00000 112.01062 125.1535
## 51.14286 114.35122 128.0303
## 51.28571  71.69412  85.8891
## 51.42857  89.23104 104.8910
## 51.57143 103.47796 120.0710
## 51.71429 100.99333 118.2095
## 51.85714 102.26554 120.1552
## 52.00000 103.49156 122.0302
## 52.14286 104.67609 123.8418
## 52.28571 105.82307 125.5959
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 4.644847 2.656620 3.713851 5.220705 5.411253 5.596717 3.301559 4.575115
##  [9] 2.679536 3.661235 5.181123 5.485866 5.809976 3.093670
## 
## $佐賀県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                 80%        95%
## 50.42857  2.3704924  1.1665219
## 50.57143  0.3052830 -0.9394394
## 50.71429  1.2879742  0.0037925
## 50.85714  2.7225108  1.4000467
## 51.00000  2.8427771  1.4831079
## 51.14286  2.9598313  1.5639483
## 51.28571  0.5979941 -0.8331866
## 51.42857  1.7726731  0.2891502
## 51.57143 -0.1936736 -1.7146586
## 51.71429  0.7189600 -0.8385865
## 51.85714  2.1713664  0.5780973
## 52.00000  2.4101074  0.7818992
## 52.14286  2.6696029  1.0071898
## 52.28571 -0.1100151 -1.8059434
## 
## $佐賀県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%       95%
## 50.42857 6.919201  8.123172
## 50.57143 5.007956  6.252679
## 50.71429 6.139728  7.423910
## 50.85714 7.718899  9.041364
## 51.00000 7.979730  9.339399
## 51.14286 8.233603  9.629486
## 51.28571 6.005123  7.436304
## 51.42857 7.377556  8.861079
## 51.57143 5.552745  7.073730
## 51.71429 6.603511  8.161057
## 51.85714 8.190880  9.784149
## 52.00000 8.561624 10.189832
## 52.14286 8.950349 10.612762
## 52.28571 6.297354  7.993283
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 31.77967 30.08000 30.75622 30.60500 29.35356 30.65212 33.34688 30.50556
##  [9] 34.39476 33.82999 35.52286 31.77590 37.29925 34.89125
## 
## $長崎県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 29.01939 27.55818
## 50.57143 27.15540 25.60721
## 50.71429 27.13689 25.22093
## 50.85714 26.73341 24.68391
## 51.00000 25.05533 22.77998
## 51.14286 26.08396 23.66572
## 51.28571 28.45322 25.86267
## 51.42857 25.24222 22.45598
## 51.57143 28.82599 25.87806
## 51.71429 27.94726 24.83312
## 51.85714 29.35730 26.09344
## 52.00000 25.33050 21.91851
## 52.14286 30.59155 27.04072
## 52.28571 27.92746 24.24105
## 
## $長崎県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 34.53996 36.00116
## 50.57143 33.00460 34.55279
## 50.71429 34.37555 36.29151
## 50.85714 34.47660 36.52610
## 51.00000 33.65179 35.92713
## 51.14286 35.22028 37.63852
## 51.28571 38.24053 40.83108
## 51.42857 35.76889 38.55513
## 51.57143 39.96354 42.91147
## 51.71429 39.71273 42.82686
## 51.85714 41.68843 44.95228
## 52.00000 38.22131 41.63330
## 52.14286 44.00694 47.55778
## 52.28571 41.85504 45.54144
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 17.65391 21.99643 19.54447 20.79357 21.43683 21.45112 19.58075 19.99410
##  [9] 20.59889 20.41229 20.41229 20.41229 20.41229 20.41229
## 
## $熊本県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%       95%
## 50.42857 12.52594  9.811355
## 50.57143 15.82149 12.552677
## 50.71429 13.15143  9.767155
## 50.85714 14.01129 10.420972
## 51.00000 14.28648 10.501308
## 51.14286 13.95073  9.980270
## 51.28571 11.74595  7.598462
## 51.42857 11.65809  7.245269
## 51.57143 11.85021  7.218932
## 51.71429 11.32633  6.516507
## 51.85714 10.98287  5.991235
## 52.00000 10.65149  5.484435
## 52.14286 10.33100  4.994285
## 52.28571 10.02038  4.519243
## 
## $熊本県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 22.78188 25.49647
## 50.57143 28.17136 31.44018
## 50.71429 25.93751 29.32179
## 50.85714 27.57584 31.16616
## 51.00000 28.58718 32.37234
## 51.14286 28.95150 32.92197
## 51.28571 27.41555 31.56304
## 51.42857 28.33012 32.74294
## 51.57143 29.34758 33.97885
## 51.71429 29.49825 34.30807
## 51.85714 29.84171 34.83334
## 52.00000 30.17309 35.34014
## 52.14286 30.49358 35.83029
## 52.28571 30.80419 36.30533
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 5.892971 4.658069 4.410165 5.408563 4.891486 5.462904 3.884319 4.379895
##  [9] 4.492559 3.594854 4.054630 4.236653 4.484577 4.007331
## 
## $大分県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                 80%         95%
## 50.42857  3.2914881  1.91434644
## 50.57143  1.5722107 -0.06134406
## 50.71429  1.0570711 -0.71794938
## 50.85714  1.8528492 -0.02943208
## 51.00000  1.1588231 -0.81712926
## 51.14286  1.5663843 -0.49630846
## 51.28571 -0.1676544 -2.31263938
## 51.42857  0.2163634 -1.98767702
## 51.57143  0.2079385 -2.06020263
## 51.71429 -0.8121608 -3.14509343
## 51.85714 -0.4731591 -2.87002567
## 52.00000 -0.4093822 -2.86884453
## 52.14286 -0.2769894 -2.79761033
## 52.28571 -0.8671091 -3.44748162
## 
## $大分県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%       95%
## 50.42857 8.494453  9.871595
## 50.57143 7.743928  9.377482
## 50.71429 7.763258  9.538279
## 50.85714 8.964277 10.846559
## 51.00000 8.624149 10.600101
## 51.14286 9.359423 11.422116
## 51.28571 7.936292 10.081276
## 51.42857 8.543426 10.747467
## 51.57143 8.777180 11.045321
## 51.71429 8.001868 10.334801
## 51.85714 8.582418 10.979285
## 52.00000 8.882688 11.342150
## 52.14286 9.246143 11.766764
## 52.28571 8.881770 11.462143
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 7.617475 8.143828 7.645195 8.117568 7.670072 8.094001 7.692398 8.072851
##  [9] 7.712434 8.053870 7.730415 8.036836 7.746552 8.021548
## 
## $宮崎県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%        95%
## 50.42857 4.464251  2.7950355
## 50.57143 4.751769  2.9561212
## 50.71429 3.953542  1.9992983
## 50.85714 4.216432  2.1512958
## 51.00000 3.509400  1.3068734
## 51.14286 3.743546  1.4405546
## 51.28571 3.110681  0.6852668
## 51.42857 3.315779  0.7975375
## 51.57143 2.745380  0.1159808
## 51.71429 2.922651  0.2063483
## 51.85714 2.405937 -0.4126718
## 52.00000 2.557231 -0.3434966
## 52.14286 2.087243 -0.9086139
## 52.28571 2.214638 -0.8593549
## 
## $宮崎県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 10.77070 12.43992
## 50.57143 11.53589 13.33153
## 50.71429 11.33685 13.29109
## 50.85714 12.01870 14.08384
## 51.00000 11.83074 14.03327
## 51.14286 12.44446 14.74745
## 51.28571 12.27411 14.69953
## 51.42857 12.82992 15.34816
## 51.57143 12.67949 15.30889
## 51.71429 13.18509 15.90139
## 51.85714 13.05489 15.87350
## 52.00000 13.51644 16.41717
## 52.14286 13.40586 16.40172
## 52.28571 13.82846 16.90245
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 2.764597 4.538358 5.676399 6.406564 6.875036 7.175607 7.368453 7.492182
##  [9] 7.571567 7.622500 7.655178 7.676144 7.689597 7.698227
## 
## $鹿児島県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                 80%       95%
## 50.42857 -1.8758609 -4.332371
## 50.57143 -1.0672938 -4.034746
## 50.71429 -0.3400823 -3.525015
## 50.85714  0.1887596 -3.102747
## 51.00000  0.5473001 -2.802401
## 51.14286  0.7812081 -2.603782
## 51.28571  0.9292475 -2.479462
## 51.42857  1.0198531 -2.406391
## 51.57143  1.0726707 -2.367637
## 51.71429  1.1008683 -2.351475
## 51.85714  1.1131237 -2.350031
## 52.00000  1.1150986 -2.358109
## 52.14286  1.1104641 -2.372318
## 52.28571  1.1015917 -2.390456
## 
## $鹿児島県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                80%       95%
## 50.42857  7.405056  9.861566
## 50.57143 10.144009 13.111462
## 50.71429 11.692880 14.877812
## 50.85714 12.624368 15.915874
## 51.00000 13.202772 16.552473
## 51.14286 13.570006 16.954996
## 51.28571 13.807658 17.216367
## 51.42857 13.964511 17.390755
## 51.57143 14.070462 17.510770
## 51.71429 14.144131 17.596474
## 51.85714 14.197232 17.660387
## 52.00000 14.237190 17.710398
## 52.14286 14.268729 17.751511
## 52.28571 14.294863 17.786911
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##  [1] 18.90966 21.28086 20.75209 20.88003 24.02531 27.81510 15.16856 20.75881
##  [9] 20.75881 20.75881 20.75881 20.75881 20.75881 20.75881
## 
## $沖縄県$lower
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##                  80%         95%
## 50.42857  5.83997842  -1.0786943
## 50.57143  7.25696332  -0.1668402
## 50.71429  5.83488893  -2.0617994
## 50.85714  5.12008444  -3.2227276
## 51.00000  7.46544700  -1.3008144
## 51.14286 10.49222413   1.3220461
## 51.28571 -2.88511384 -12.4421527
## 51.42857  0.90972699  -9.5977410
## 51.57143 -0.07085991 -11.0974192
## 51.71429 -1.00731507 -12.5296038
## 51.85714 -1.90510961 -13.9026620
## 52.00000 -2.76866975 -15.2233633
## 52.14286 -3.60163651 -16.4972760
## 52.28571 -4.40704793 -17.7290465
## 
## $沖縄県$upper
## Time Series:
## Start = c(50, 4) 
## End = c(52, 3) 
## Frequency = 7 
##               80%      95%
## 50.42857 31.97935 38.89802
## 50.57143 35.30476 42.72856
## 50.71429 35.66929 43.56598
## 50.85714 36.63998 44.98279
## 51.00000 40.58517 49.35143
## 51.14286 45.13798 54.30816
## 51.28571 33.22224 42.77928
## 51.42857 40.60789 51.11535
## 51.57143 41.58847 52.61503
## 51.71429 42.52493 54.04722
## 51.85714 43.42272 55.42027
## 52.00000 44.28628 56.74098
## 52.14286 45.11925 58.01489
## 52.28571 45.92466 59.24666